In this article, I will delve into whether artificial intelligence (AI), particularly the emergence of tools like generative AI (GenAI), will replace classical software outsourcing as we know it.

AI vs software outsourcing: An opportunity or a threat?

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Get the most recent info and news about Scale Unlimited on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Scale Unlimited

In May 2019, announced a partnership with Provable, a very efficient oracle that produces reliable real-time prices that the framework uses today to calculate critical levels every six minutes. Since this time, the project has also engaged in further research and development to reduce third-party risk, and improve the quality of its price feeds for EOSDT position owners and NUT (the framework’s utility token) holders. Equilibrium Quality price feeds help position owners make informed decisions for accurate valuation and sound management of their positions and risks. It also guides NUT holders through the liquidation process: should closely monitor their collateralization ratio. If the ratio drops below the 170% minimum collateralization threshold, the owner will suffer a 20% liquidation penalty. The EOS price feed determines the current dollar value of the collateral, which in turn is divided by the dollar value of the EOSDT debt (assuming 1 EOSDT = 1 USD) in order to calculate the collateralization ratio. - Position owners can buy EOS collateral with NUT at a discount, then buy and redeem the bad EOSDT debt. In other words, they are de-leveraging the Equilibrium Balance Sheet. This price-dependent process must be swiftly executed to effectively minimize Equilibrium’s insolvency risk. - NUT holders The EOS price feed needs to be accurate and reliable. If not, the consequences on a contract’s output can be catastrophic. The great oracle problem Synthetix, a service that provides fiat currency stablecoins, experienced serious issues in June 2019 due to a mispriced Korean Won (KRW) rate in its private oracle. In normal conditions, the reference price produced is based on the average of several sources. After an outage that removed most price feeds from the equation, the reference price produced became for a short period of time an average of two prices, ! “resulting in several trades with profits of 1,000x”, representing “over $1 billion in profit in less than an hour” Source: https://coinmarketcap.com/currencies/synthetix-network-token/ Let’s consider a hypothetical scenario where the initial collateralization ratio is at 170%. Close your eyes, open them a few minutes later, and imagine that EOS price has declined more than 45% in that short time. The resulting collateralization ratio is now below 100%! Equilibrium is insolvent and the oracle failed to warn the framework in time! A drop like this hasn’t happened on EOS yet, but there is always a chance that it could happen in the future. Let’s imagine a further worse scenario in which the oracle feed isn’t delayed, but incorrect, like with Synthetix. Whichever decision the smart contract makes will not only be wrong, but will usually be irreversible too. The problem we’ve been describing so far is “the great oracle problem.” If we feed outdated or incorrect data to a smart contract, that contract’s output will likely be incorrect as well: garbage in, garbage out. Medianize it One solution is to use not one but multiple external price feeds. The robust median value of reported prices would define a reliable, more accurate reference price than the average. It isn’t affected by outliers, therefore it would disregard any outdated or erroneous price as long as most of the price feeds are neither outdated nor erroneous. In any case, this is no guarantee that there won’t be any mispricing down the road, but it’s an important step in the right direction. For instance, the was introduced by MakerDAO to produce such a . medianizer smart contract reference price Source: https://mkr.tools/system/feeds The inclusion of several sources, combined with the choice of the median as a reference price, drastically improves the oracle’s quality. In the case of MakerDAO, the triggers for a reference price update are either: - a difference between the last reference price and the updated median price that’s above 1% - an update time that comes more than six hours after the previous price update. These trigger conditions are scattered in value or time (at least 1% or six hours apart) for good reason. The process of running the medianizer contract and using its output to update other smart contracts (e.g. the liquidation contract) comes at a . cost Source: https://www.eosrp.io/ In EOSDT’s case, the resources required to scale the process for thousands of users can be costly and limited even though the EOS blockchain can theoretically support unlimited scale and speed: the current is 0.0851 EOS/KB, and the current CPU price is 0.00236 EOS/ms/day. RAM price The RAM supply is also currently capped at 129.13 GB. These constraints drive dApp developers to look for affordable and effective solutions. Equilibrium’s partnership with LiquidApps Equilibrium and LiquidApps are partnering up today with the ultimate objective of implementing a scalable, cost effective on-chain rate medianizer for better event monitoring. As a first step, Equilibrium refactored its rates table to incorporate the EOS and NUT prices quoted in several currencies from multiple sources. Acting as arbitrators, NUT holders need to observe timely and consistent EOS and NUT reference prices to determine whether they should handle the liquidation, or expect to handle it very shortly. Timing is always critical in a market that will more likely be volatile when significant liquidations are triggered. A swift procedure brings more certainty in an arbitrator’s ability to turn the discount on his purchase of a bad debt’s collateral, into a realized USD profit. To alleviate the arbitrator’s duty even further, Equilibrium is adding an on-chain function that automatically liquidates the position with the lowest collateral ratio if it stands below the critical level. This function acts as a strong safeguard against insolvency risk. Its input is a table of positions sorted by their collateralization ratio, that is updated with every new event by the . eosdtcntract In time, this table can not only spot and liquidate the under-collateralized positions, but also act to preserve a position (similar to MakerDAO’s “CDP saver” feature) by warning position owners of forthcoming liquidations. Follow Equilibrium | | Website Twitter Telegram

A Guide For Stablecoins Owners On Handling Liquidation - Accurate Prices Matter

DynamoDB can be hard to grasp if you come from the relational database world — but the benefits for serverless apps are too good to ignore. In the path to serverless, one of the hardest parts has been getting to grips with DynamoDB, the mysterious Amazon NoSQL database that’s so simple to learn it takes six months to master. It’s a beguiling, frustrating, wondrous tool that alternately has me crying with joy or yelling obscenities at my mouse. I’ve worked all my career with various relational databases, so it’s been to hard to walk away from baked-in patterns and practices and try to think about data in a completely different way. I’ve read many pieces about , , and if you try it, . But we stuck with it and found it was essential for our serverless data needs. how it wasn’t for everyone possibly isn’t for anyone you’ll end up switching back to RDS Credit: https://dzone.com/articles/cloud-data-services-sprawl-its-complicated Why not just use RDS? There are a few reasons why relational databases and serverless don’t mix so well. First, if your function scales up dramatically — and it can, that’s the whole point — classic databases aren’t typically designed to handle large numbers of new connections appearing. Back in the good ol’ days of when we had servers, these connections were shared by many end-users since the middle layer might use a single connection per server. This isn’t necessarily the case in the FaaS world, where your provider can spin up new instances of your function as needed and you might more connections appearing than you expected. Second, database connections use ‘keep alive’ pings to, well, keep the connection alive — but your Lambda function freezes when it’s not in use so this stops. To the database, this looks like the connection has gone away when it stops responding. In using RDS, you’re forcing a connection method that natively just doesn’t work well with Lambda. Third, relational databases don’t scale automatically. Many have phenomenal architectures and ways to handle scaling but it’s not automatic. If you’re in the business of building scalable web/mobile apps, you can spend much more time managing a database that you might think. DynamoDB is a truly managed option when the design is right, and you’ll really appreciate this if you’re coding an app where the usage is spiking. Still, RDS usage is probably fine for low-use serverless apps but if you have any potential to scale up quickly, it’s all a little tenuous, compared with the drop-in “it works” world of DynamoDB. It’s possible that Aurora Serverless may bridge this gap when it gets out of technical preview, but so far there’s no indication that the interaction will be different. The benefits of DynamoDB for serverless. The from 2007 explains the main objectives of the platform in Amazon’s e-commerce store. High availability and consistent performance are the top two features, and these are achieved at the cost of ‘traditional’ database properties such as consistency and transaction support. original DynamoDB paper As a result, DynamoDB shines when providing data support in serverless apps where the data needs are separated by user — user profiles, shopping carts, private collections of items. Your users can access and update these items at practically unlimited scale and your only choke point will be the throughput allocated on a table (which itself is auto-scalable). If you’re planning to launch a web or mobile app with potentially rapid growth, DynamoDB’s primary design features will serve you well. You don’t have to worry about managing connections, scaling or latency — it will, for the most part, ‘just work’. Still you have to be careful where users are accessing common data, even in simple cases— lookup tables or counters, for example — since the partitioning and throughput model can cause some unexpected side-effects. The World’s Fastest Primer on DynamoDB. DynamoDB is designed to give you very consistent performance in an almost maintenance-free way if you create the tables properly, so it’s a natural fit for serverless apps (yet surprisingly hard to get right): Compared to relational databases: tables are tables, records are items and fields are attributes. An attribute can be a flat or nested JSON structure. Each item can have different attributes but no attributes can be a zero-length string. The size of the table is potentially unlimited but access patterns are important to make that work well (the ‘hot key’ problem). You can change or update any data except the partition and sorts keys, and your choice of those keys is a critical design choice. The keys form a hash that you need to ensure is randomly accessed throughout your data set, ideally. You pay for throughput (RCUs/WCUs) and storage space used. When you query on an item, the size of the item matters since you’ll be charged for the whole thing (even if you filter out or return a subset of the data). Local Secondary Indexes must use the original partition key (but different sort key) and must be defined when the table is created. They also share the table’s throughput (often forgotten). Global Secondary Indexes can be created later on a new hash key but use their own throughput. Hashes do not have to be unique. The naming is unhelpful and neither index works the way you would expect. There are no traditional SQL query operators like sum, average or count, no group by clauses. Mostly it’s (projections) (tables) where (partition ID, sort key and filters). And then the rest is up to you. select something from somewhere some condition is satisfied Consistency is something you pay for if you really need it. Without it, you might find a query returns stale data if an update happened. In many cases this doesn’t matter but you wouldn’t want to run a banking app this way. just Some query patterns work really well — shopping carts, for example. You have a single item in a carts table mapped to a user ID, containing a nested JSON list of items in the cart. It’s great because it will scale easily, the lookup will be blazingly fast and the eventual consistency model shines in this kind of use case. Even some classic relational database patterns are fairly obvious — like returning a list of orders for a customer. Here you simply make the customerId the partition key, the orderId the sort key, and you can quickly pull the list of orders. But… if you want to know how many orders or the average size of an order… well, we can’t do . In fact we can’t even tell you the total number of items in the table without a table scan or an insanity-induced counter system. Welcome to the upside-down world of DynamoDB. that Think of your DynamoDB table as a filing cabinet Coming from relational databases, I love tables and believe they can solve most of the world’s ills (well almost). But what DynamoDB calls a table doesn’t seem much like a table — it doesn’t have columns, required values (beyond the keys) and the indexing behavior reveals it’s not much like a table. I think it’s more like a filing cabinet. Imagine this: DynamoDB is run by the world’s fastest librarian — Alice — who bends the laws of physics and the Dewey Decimal System, capable of finding and organizing your data in a dependably snappy way. When you create a table, you tell the librarian two things — an ID (partition) and a range (sort key) — and she sets up some space for you. A DynamoDB table is a filing cabinet where the partition key highlights which drawer to search, and the sort key refers to the number of files in that drawer. The file itself can contain many documents or nothing. But critically when the librarian comes back with your files, she doesn’t look inside, peek at the data or even care what’s there. The size of the drawer is limited (10GB), and can only be opened and closed so fast (3000 RCUs/1000WCUs) of the number of files. If you go over these limits, Alice will find you another drawer. But once you have more than a single drawer, you will shrink back to one drawer. regardless never Alice will always find space for your information but is motivated by charging you for the combined weight of all the files brought back for each of your requests. The librarian does not care if the files contain things you didn’t want. If you tell your librarian from the outset that you need another index, you’ll be given an LSI — this has the same partition key (drawer) but allows you to put more than one label on each file. This is why the LSI uses the same throughput resources as the main table, because it’s part of that table. But once you have an allocated filing cabinet, what happens when you ask Alice for another index? You’ve already been set up so the librarian does the next best thing — she finds you another filing cabinet that contains a read-only duplicate of some of the items in the main drawer. This is why GSIs use additional resources outside of the table, and why you cannot update items retrieved via a GSI. It’s a brave new world. For our team, learning serverless apps has been about discovering the right tools for the job. DynamoDB has provided a critical piece in allowing consistent performance and reliable scaling in customer-facing data access, but there is a significant learning curve in getting the design right.

The upside down world of DynamoDB

State of Cryptocurrencies: Summer 2018

Welcome to HackerNoon’s interview series. This section is curated by our Editorial team to spotlight bold and disruptive startups across the globe. If you’ve been nominated, and answer these questions Startups-Of-The-Year create a brand profile here. Hey Hackers, HarperDB has been nominated in HackerNoon's annual Startup of the Year awards in Denver, USA! Please vote for us here: https://hackernoon.com/startups/north-america/north-america-denver-co-usa Read more about us below to understand why we deserve your vote. Meet HarperDB HarperDB is a database and application development platform that is focused on performance and ease of use. With flexible user-defined APIs, a simple HTTP/S interface, and a high-performance single-model data store that accommodates both NoSQL and SQL workloads, HarperDB scales with your application from proof of concept to production. Install and manage on your hardware, or have us host it with . HarperDB Cloud Fun fact: HarperDB was named after our CEO's pup named Harper! We believe dogs symbolize loyalty, trust, and dependability, so we took these characteristics and built the brand around Harper and all of our other loyal employee pups. :) My Role I’ve worn a few different hats in my 4+ years at HarperDB, and currently am the Head of DevRel & Partnerships. This includes community engagement, content creation, and supporting our users to ensure that they have everything they need to be successful. I also manage our partnerships from a high level, coordinating training, events, timelines, etc., to make sure both parties are working towards their joint and individual strategic goals. I’m also the host of the and have written a . 😁 Select* podcast few articles How We're Disrupting/Improving the Software Dev/Data Management Industry HarperDB is disrupting the data management industry in a few ways. HarperDB reduces complexity, increases performance, and lowers costs by combining user-defined applications, a high-performance database, and an enterprise-grade message queue into a single package. The platform offers unlimited horizontal scale at the click of a button and synchronizes data across all the nodes in a cluster in milliseconds. HarperDB simplifies the process of delivering applications and the data that drives them to the edge, which dramatically improves both the user experience and total cost of ownership for large-scale applications. Deploying HarperDB on global infrastructure enables a CDN-like solution for enterprise data and applications. Standing Out From the Crowd - Streamlining the Journey to the Edge Even though edge computing is becoming mainstream, no one is talking about the complexity of deploying distributed applications and data to the edge. There are a lot of great articles about the edge being the future, and for the most part, edge companies are addressing this demand similar to how companies addressed the cloud. However, the edge is a fundamentally different problem. Once you consider the exponential complexity of building multiple data stores, deploying APIs that load balance across geo-distributed data stores, and the sneaky complexity of keeping everything in sync, there are very few companies that have actual solutions for the edge that won't stifle future innovation with mountains of system complexity. HarperDB is the only solution that actually decreases complexity while moving systems to the edge, and the only platform that gives innovation room to flourish while moving to the edge. Our Predictions/Thoughts on the Software Dev/Data Management Industry in 2023 2023 is poised to be a breakthrough year for Unified Backend technology in the Software Development Industry, heralding a monumental shift that will forever change the landscape of development. This transformative innovation blurs the line between edge and cloud, effectively commoditizing infrastructure. Unified Backend technology combines the finest elements of best-in-class database management, API server functionality, and data streaming capabilities, unifying their underlying systems at the core. Imagine a world where developers no longer grapple with the complexities of integrating disparate backend components, but instead embrace the power of a Unified Backend. In this new era, ease, scalability, and unprecedented performance take center stage. Applications seamlessly tap into a unified system processing data at the edge or sending real-time messages. The result is a paradigm shift, enabling faster development cycles, reduced latency, and lower infrastructure cost than ever before attainable. We predict that embracing the transformative power of Unified Backend technology is the key to forever changing the way software development is approached and executed, shaping the future of the industry. Why We Decided to Participate in HackerNoon’s Startup of the Year Awards We decided to participate in the awards because we love the HackerNoon community, and who doesn’t like a good ole’ friendly competition? Final Thoughts Thanks for reading! Please feel free to reach out with any questions/comments; we would love to hear from you. Vote for us today! https://hackernoon.com/startups/north-america/north-america-denver-co-usa

Startups of the Year 2023: Meet HarperDB - A Database and Application Development Platform

A Comparison of Their Universal Revolutionary Impact Distributed Ledger Technologies (DLT) Worldwide finance is changing. As computers and digital technologies trickle into our modern life, there is change everywhere. There is no better example that being used in both hashgraph and blockchain currently. Distributed Ledger Technology (DLT) Your expectation should be a comparison between blockchain and hashgraph, which means you know what DLT is. For those of you who want a revision: is a new system of keeping transaction records in a way that solves the (Google it). Distributed Ledger Technology triple entry book-keeping problem This solution was first proposed with the research paper published in 2008 by a man with the pseudonym ‘ . bitcoin cryptocurrency Satoshi Nakamuto’ That paper was a decade ahead of its time. In traditional accounting, records were centralized with multiple layers of security. In DLT, records are replicated in numerous nodes. Yes, you read that right, replication. The data integrity is protected by a mechanism of cryptographic hashing. That guards against fraud and theft. The replication prevents falsification. The chained hashing guarantees integrity. And the distributed nature of the algorithm guards against DDoS attacks. What do all those facts imply? Well: just that a centuries-old accounting system is going to be replaced. With what? Completely automated transaction record-keeping ( . smart contracts) And that too with a fraction of intermediary agencies compared to what we have now*.* ! The world is going to change Classical Blockchain vs. Hedera Hashgraph. Satoshi Nakamoto — Wikipedia Bitcoin Protocol Inventor The identity of the inventor of the Bitcoin cryptocurrency and the blockchain implementation to support it has always remained a mystery. We know that under the pen name of ‘Satoshi Nakamoto, on 31 October 2008, a link to a paper titled was posted to a cryptography mailing list. Bitcoin: A Peer-to-Peer Electronic Cash System Who Satoshi Nakamoto was is a perennial conspiracy theory topic. However, regardless of all the secrecy, the cryptocurrency was issued and the blockchain industry adopted it. While blockchain has several issues – transaction times, latency, excessive energy consumption – the idea of a decentralized financial entity scenario was a novel discovery. The rest, as they say, is history. Swirlds (Hedera Hashgraph) Founder Swirlds enables developers to create distributed applications with unlimited scope and scale. Leemon Baird is an innovator who worked in Distributed Ledger Technology and understood its long-term scope. He spent two years designing and perfecting a distributed voting consensus algorithm that was , at that time, a first in the field. Asynchronous Byzantine Fault Tolerant Along with Mance Harmon, he founded the Hedera Hashgraph company and now many of the largest and most influential companies in the world like Microsoft, Google, and IBM are among the governing council that regulates Hedera Hashgraph. Skeptics maintain that the performance claims are yet to be tested in the industry. However, a preliminary investigation has been massively positive in favor of the claims. And if the numbers and statistics are supportive of the claims, then watch out, because many things will change. Accessibility Blockchain Blockchain has been around for 10 years. Hence there are many companies using it in the industry, as either public (any applicable user), private (within companies) and hybrid implementations (variations of both together). Despite all of blockchain’s problems (latency time, inefficiency, slow transaction speed), a number of corporations run blockchain solutions. Hashgraph Hashgraph is a new phenomenon and is just beginning to take off. They can be strictly private, or, by using the company Hedera alone, public. Leemon Baird holds the patents to the hashgraph algorithm and gossip about gossip mechanisms. This may seem like a restriction for open-source advocates. However, licensing the technology adds stability and reliability in the future to companies that will use it. Consensus There are a lot of blockchain solutions in the market. Bitcoin Consensus Bitcoin runs on Proof-of-Work. PoW is basically solving a cryptographic algorithm problem by every competing node in the blockchain. The first to find the solution is awarded the reward in bitcoin. This means that all the work done by the millions of competing nodes have gone in vain. The inefficiency is massive! Ethereum has shifted to Proof-of-Stake. PoS means that the amount of cryptocurrency you can mine is based on the amount you already have. Unlike PoW, the reward comes in the form of transaction fees. There are many variations of these two concepts in the industry according to each cryptocurrency. Hashgraph Consensus and Consensus-as-a-Service Consensus is the killer feature of the hashgraph algorithm technology. Hashgraph uses virtual voting through the gossip about gossip to build a directed acyclic graph (DAG). Using an algorithm that is Asynchronous Byzantine Fault Tolerant by design (Leemon Baird devoted two years to work on this concept),  - - when  over two-thirds of the nodes agree as to which node on which a transaction should be stored, it reaches finality and the new transaction is added to the graph. Thus, there is no mining and no waste in energy resources. Consensus-as-a-Service is where Hedera Hashgraph truly shines. The Hedera Consensus Service can be ‘plugged into’ any DLT network or blockchain that requires a consensus mechanism. This is a historic milestone in cryptocurrency and DLT technology. Expect Hedera Consensus Service to be used in many distributed ledger technologies. It solves their biggest problem in the most optimal and efficient way. Efficiency Bitcoin Mining Bitcoin wastes energy on an exponential scale. At any given time, there are millions of Bitcoin mining devices, all solving the same problem (a subset of problems) with hash cryptography. The first one to the solution is given the reward. All the other miners? Used energy redundantly, since there can only be very few winners among a massive number of miners. When computing cooling became too expensive to offset with the price of Bitcoin, many in the industry shifted to Iceland. Currently, mining is performed using ASICs (Application Specific Integrated Chips) that are not only expensive – they also take a lot of power. Currently, the cost of Bitcoin mining daily is as much as the entire country of Switzerland. And most of that work will be useless, since it’s a winner-take-all situation. Environmentalists have called Bitcoin mining ‘the biggest energy disaster of the 21st century’. Because of the current energy crisis, Ethereum shifted to a PoS (Proof-of-Stake) mining mechanism. Only time will tell what the future holds. Hashgraph The hashgraph is actually an unfair competitor in the efficiency category. It was designed mathematically in such a way that all calculations would be optimal, and the efficiency is exactly 100% - no more, no less. The Gossip about Gossip mechanism assigns transactions to nodes with minimum processing power. Efficiency was one of the key considerations in Dr. Baird’s agenda during the design of Hashgraph. For this reason, Hashgraph is sometimes referred to as Blockchain 3.0, with Ethereum as Blockchain 2.0, and Bitcoin as Blockchain 1.0. Speed of Transactions Blockchain One of the biggest problems that blockchain faced when dealing with live systems was that transaction speed was slow. This meant that it would not be uncommon for a Bitcoin transaction to arrive at finality (time taken for a block – a new transaction – to be added to the chain) to be well above 3 hours. Ethereum had a finality time in minutes. In real life, if blockchain was going to be used in all the industries in which it held such promise, it would need to process at least 25,000 transactions per second. Blockchain, as a distributed verified transaction storage system, could be applied almost anywhere. But if the transaction speeds and finality times were this long, there would be no chance of it ever being a practical replacement to existing systems. At least – until Hedera Hashgraph came along. Hashgraph One of the biggest features of hashgraph was the speed at which transactions could be processed. While the figure on the website is 500,000 transactions per second, in practice, the speed is limited only by the bandwidth and the number of nodes in the network. This changes everything. Now any industry that requires data storage, smart contracts, cryptocurrency, cloud file storage, or transaction processing can use Hedera Hashgraph tech and gain decentralization, automation, lesser transaction costs, less manpower requirements. In a nutshell, all the benefits that come with blockchain. Large numbers of dApps are already released on the Hedera mainnet, and 26 companies participated in the initial launch. The world is changing. If we want to adapt and survive or even thrive, so must we. Hedera Hashgraph – A Better, Faster, Practical, Secure, Cheaper, Simpler Blockchain I sincerely hope that this has been an illuminating read for you. For once, the popular picture is wrong. Hedera Hashgraph is still a project of infinite potential. Hedera Hashgraph really does give you all the benefits of blockchain without any of its drawbacks. And we can expect variants of DLT to also appear in the market. There are only two directions. Forward - And upward. The tokenization concept in cryptocurrency gives you unlimited power to do or accomplish practically any cause, any passion, any business, and any charity. There has never been a more opportune time to be an entrepreneur, especially in the Hedera Hashgraph space. And the big companies are sitting up and taking notice. When Google and IBM both take such interest in any technology, it means that its future is bright. Very bright. Just as apps on mobiles became the biggest consumer industry for IT, now, (distributed apps) running on blockchain will completely transform the way we live and breathe and sleep and eat. dApps dApps are the future. It has never been a more exciting time to work as a blockchain developer. All the best to a long and fulfilling career! The financial world will be turned upside down. If regulations don’t cause stagnation and world war 3 doesn’t start, that is! Also Published Here

Classical Blockchain vs. Hedera Hashgraph

Imagine a world where you can launch ad campaigns on time without slogging on weekends to meet deadlines. Your designers feel fulfilled at work. Your prospects find your email creatives relevant and personalized. The return on investment (ROI) of your advertising efforts improves massively in a few days. Sounds like a dream, right? But it’s also one that’s within the realm of possibility. Even I felt skeptical when I discovered — a system to create 1,000s of pixel-perfect videos, creatives, and banner ads using automated customization. Creative Automation This article will explain what creative automation is and how it can make your life easier by saving time, money and removing repetitive manual work. What is creative automation? Creative automation is the idea of using technology to simplify the modification of creative assets like video or banner ads using automation. It removes the repetitive, manual effort needed for customization at scale and empowers teams to produce thousands of high-quality video or banner ad variations from a base template in minutes. Businesses that need to design or customize a large number of video or static creative assets frequently benefit from using this automation tech. It can help companies in marketing localization while ensuring brand consistency globally. It also reduces a campaign’s time to market while lowering the marketing costs. How does creative automation work? Creative automation software applications make it easy to create, optimize, and manage visual content. They can rapidly create multiple versions of a base template — both static and video — using automation. Designers create the base template using tools like . Then, dynamic elements (text, image, audio, background, etc.) are identified for customization. Teams or individuals then map each component to a field in a linked spreadsheet or a type of data repository. A change in the spreadsheet causes elements like text or audio to change simultaneously. Adobe After Effects Note: people often confuse Creative Automation with Dynamic Creative Optimization (DCO) but they are not the same video gives an overview of how most creative automation platforms help companies do faster content versioning and content localization. This What are the benefits of using creative automation? A large percentage of marketers and designers are unaware of creative automation solutions. As per a , only 18% of digital advertising decision-makers have completely automated processes for content versioning. study Manually doing large-scale content versioning is like driving 10 km to the same burger joint every time you feel lazy to cook instead of ordering via an app. In the end, you spend more, waste time, and feel exhausted. Whether you version your creatives manually or use automation, content is the center of today’s digital experience. Companies that can produce high-quality and engaging content at scale increase the odds of grabbing the audience’s attention. However, generating all of that content can be an arduous process if it involves heavy editing. Creative automation comes into play and removes production bottlenecks by automating content versioning. Let’s look at some significant benefits of automating creative production. 1. Reduces production costs Producing video and banner ads at scale makes up a significant portion of an advertising budget. Imagine being assigned the responsibility of executing a global marketing campaign. Your boss has allocated “ ” to promote a recently launched phone — and it’s not Samsung or Apple. He has his head in the cloud and aspires to connect with regional audiences for faster growth. enough budget When you got this opportunity, your energy level touched the roof, but you soon realized you were biting off more than you could chew. The number of ad variations needed for successful campaign execution causes more anxiety. The idea of hiring an agency for a timely campaign launch causes more stress. You could have lessened this stress if you had the right tools (or unlimited budget) to scale creative production; while simultaneously connecting with regional audiences using personalization. Manual customization is time-consuming and costly. Earlier, the headcount needed for content localization would increase with the number of variations. More employees mean more salaries. By automating content versioning of video and static creatives, you can reduce your production costs. Start with one base template, let’s say a video ad, then swap the text or sound to deliver personalized video ads to your viewers. Money allocated to hire more people for boring, repetitive customization can be spent elsewhere. 2. Empowers you to launch campaigns faster In today’s competitive environment, the ability to be in the right place at the right time has become difficult. When Cristiano Ronaldo gestured people to drink water instead of Coke during a press conference, companies quickly cashed in on the opportunity. They subtly promoted their products using moment marketing. The effort would have multiplied for a global brand with the tweaks needed to align with different regions. I’m not a designer, but I’m sure the thought of customizing creative assets repeatedly in such instances will cause a headache. A shows 64% of creative teams spend at least 5–10 hours per week on mundane edit requests. study Creative automation tools help you quickly launch campaigns for different audiences and goals. Automation can make your life much easier, whether you’re running a personalized email marketing campaign or retargeting prospects using ads. You can customize a base template into thousands of pixel-perfect variations based on data or content localization goals. Most content versioning platforms also evade unnecessary feedback loops. They have the functionality to get approvals/internal reviews on the asset within the application and share them directly on social channels. 3. Ensures brand consistency worldwide Marketers and designers spend hours upon hours creating and fine-tuning their marketing assets. When brand consistency directly , teams can be oblivious to the hours they waste on manual editing. contributes to revenue growth Creative quality defines . 75% of advertising impact Still, they often have a difficult time staying consistent across different marketing channels and countries. It happens because each channel or region uses creative assets from multiple sources. Most creative automation tools help ensure brand consistency by enabling you to lock dynamic elements of your assets. Font, color, design can be disabled for editing when a video or banner needs to be customized. Let’s take the example of Coca-Cola. Do you associate red color with Coke or McDonalds? Coke, right? Suppose the beverage manufacturer needs all its creative assets to be published with red color, a specific font, and avoid accidental localization blunders by regional teams. In that case, they can disable change of color in the versioning software and reduce the possibility of a human error. 4. Helps in marketing localization and personalization Colgate once toothpaste in France named “Cue” without realizing that it’s also the name of a French pornographic magazine. launched Such incidents happen when you don’t involve a regional team while marketing your business in a foreign land. Now, such blunders would be more destructive for a brand — thanks to the internet. Regional employees or agencies are familiar with the local nuances, culture, and language. They can help you create assets that touch the right chord with your consumers. And for most regional marketing campaigns, creative assets don’t need to be created from scratch. They only need customization. But companies are skeptical about giving complete creative control to local agencies to ensure brand consistency. Creative changes made by regional teams go through multiple stages of approval before going live. Creative automation tools can speed up the content localization process by removing unnecessary approvals and avoiding creative blunders. As mentioned earlier, they have the functionality to restrict editing of dynamic elements like font, color. Also, these elements can be changed at scale rapidly using a linked spreadsheet or a data repository. No manual work, less scope of human errors — hence fewer stages of approval. Companies can also use this automation tech to personalize their marketing. For example: to build different versions of emails with personalized information about each customer’s account or interests based on past interactions with the company (for example, adding products to a cart or filling a form). Once again, you only need to provide input once or link the tool with a data repository that’s dynamically updated and let the software do the rest. Final Thoughts Most marketers agree people hate seeing the same old and boring ads repeatedly. But companies cannot afford to create original content for each user. Instead, they make manual modifications on a base creative  for various audience segments and content channels. But this approach doesn’t work too well because old-fashioned customization is slow, inefficient, and dull. It also increases the odds of human errors and can embarrass a company. The productivity of work is not the responsibility of the worker but of the manager - Peter Drucker Many creative automation tools, like Ad-Lib or Celtra (no affiliations to these companies), empower brands and agencies to create customized creatives at scale. It vastly decreases production costs and helps launch campaigns faster. They also help ensure brand consistency and help you connect with your customers using marketing localization and personalization. If you or your organization needs to design or customize a large number of video or static creative assets, it may make sense for you to use this automation tech.

Creative Automation is Shaking Up the Digital Marketing Game

It’s easy to write a post about the general efficacy of AI in the enterprise, this post seeks to delve into how an enterprise can quickly and effectively start seeing ROI from machine learning. Photo by on Markus Spiske Unsplash Let’s face it, machine learning and AI are here and they are a force to be reckoned with. Why is that? Because machine learning represents the biggest return on investment in a generation. For a primer on what AI, machine learning, neural net and other terms mean, . read this post Ok, lets talk business. The reason why you’re reading a lot about machine learning and AI today is because of a somewhat silent revolution in computing. For a while, Moore’s Law seemed to have broken down as major advancement in CPU power plateaued. Interestingly, there was a different type of processing technology called a GPU (graphical processing unit) that ended up being really good at handling the same kinds of calculations that neural networks require. GPU processing power has exploded over the course of the last few years, and as such, the reality of using neural networks in a manner is upon us. practical https://www.wired.com/wp-content/uploads/archive/ly/wired/news/images/full/supercomputer2_f.jpg I emphasize the word practical because neural networks and machine learning have been around for a very long time, but the time and resources required to process a face recognition system (for example) was extremely cost prohibitive. Face recognition at scale The relative low cost of GPUs has suddenly made powerful machine learning a viable technology for business. But Machine Learning is still hard In spite of the growth of excitement around machine learning and AI, it turns out that its actually still very difficult to implement. You need specially-trained machine learning engineers, PhD data scientists mathematicians, and managers who understand the complexities and limits of machine learning. For enterprises, this is still an expensive proposition. Photo by on jens johnsson Unsplash Having trained machine learning models my self, I can tell you that the hardest part of building something useful is gathering the training data. In some cases, you need thousands upon thousands of examples of the different categories of data you want to train the model with. You have to make sure the data is in the right format, and doesn’t have too much variation. Once you’re happy with your data set, you have to make one of hundreds of decisions about what kind of processing you’re going to use to get the results you want. And you’re usually wrong the first time you try it — so be prepared for lots and lots of tests. 6 months later, you’ve got a model you’re comfortable with but now you need to figure out how to deploy and scale it in production. That’s a whole other discipline outside of machine learning that you need to have experts on in order to have something viable. Enter the cloud companies Computing in the cloud has its benefits, namely you only pay for what you use. Theoretically, you no longer have to pay for air-conditioning, equipment replacement, and physical space in your data center to power your service. That should be a huge savings. Its the op-ex revolution! But it turns out there are still issues with the cost of moving content back and forth, the incredible growth in data, vendor lock-in and complexities with security. When the cloud companies started offering machine-learning-as-a-service (MLaas), enterprises ran into a similar barrier. I personally have heard customers complain about the same two things when it comes to integrating cloud-based machine learning; 1) Its too expensive at scale because they charge per API call and 2) I don’t want or can’t have my data leaving my premises. On premises machine learning said it best in his entitled GARY ORENSTEIN Venture Beat article Using AI to find bonus value hidden in your company’s data: Large enterprises already have numerous applications and data repositories in place today. However, with a broad business focus on digital transformation, the time is right to pursue new data applications and incorporate machine learning and artificial intelligence. Given the resources available in the cloud from Amazon, Google, Microsoft, and others, it often makes sense to start there first. But companies should keep in mind that the ability to deploy solutions on any cloud or in their own data centers may be an important prerequisite to optimally service their companies long term. Enterprises need to be masters of their own machine learning, in affect, building an internal MLaaS solution to suit their own needs and drive customer value. Let me give you an example. One customer of my company works at an enterprise in the healthcare sector. He explored building a machine learning team and discovered it was too expensive. He then looked into IBM Watson, but told us that not only was their text analysis capability less accurate than , but they were going to be charged a fortune to use it. Machine Box Machine Box’s What he needs to do is free up his team from manually pouring over thousands of pages of text every day so they can focus on their primary jobs (a perfect use case for machine learning by the way). What changes the game for enterprise is being able to integrate powerful machine learning tools to save time, create new products, generate revenue, and overall derive tremendous value from the endless data being created, managed, and processed, in a cost-effective way. Remember, this is about ROI above all else. Enterprise-scale, simple APIs, and unlimited use The enterprise is going to need all three. Fortunately, that is exactly what Machine Box does. What is Machine Box? puts state of the art capabilities into so developers like you can easily incorporate natural language processing, facial detection, object recognition, etc. into your own apps very quickly. Machine Box machine learning Docker containers The boxes are , so when your app really takes off just add more boxes horizontally, to infinity and beyond. Oh, and it’s than any of the cloud services ( )… and . built for scale way cheaper and they might be better your data doesn’t leave your infrastructure and let us know what you think. Have a play

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